Extreme Rainfall Analysis Of The West Papua Province Using Schlather’s Model Of Max Stable Process

Alya Azzahra; Pratnya Paramitha  Oktaviana; R. Mohamad  Atok

doi:10.20956/j.v20i1.27433

Authors

Alya Azzahra Departemen Aktuaria, Fakultas Sains dan Analitika Data, Institut Teknologi Sepuluh Nopember
Pratnya Paramitha Oktaviana Departemen Aktuaria, Fakultas Sains dan Analitika Data, Institut Teknologi Sepuluh Nopember
R. Mohamad Atok Departemen Aktuaria, Fakultas Sains dan Analitika Data, Institut Teknologi Sepuluh Nopember

DOI:

https://doi.org/10.20956/j.v20i1.27433

Keywords:

Spatial Extreme Value, Max Stable Process, Schlather's Model

Abstract

Data from Badan Pusat Statistik (BPS) in 2021 notes the province of West Papua as the province with the 5th highest rainfall in Indonesia with a rainfall of 3,811 mm. The province also recorded 268 rainy days, the most amongst all provinces in the country. The excess amount of rain is one of the causes of disasters such as floods. This research uses rainfall data from the Regencies of Manokwari, Fakfak, and Kaimana. The method used is Spatial Extreme Value particularly Schlather's Model of the Max Stable Process. The data used is hourly rainfall for the period of 13 March 2022 to 17 October 2022 with the proportion of training and testing data respectively 85.84% and 14.16%. Extreme data collection was carried out using the Block Maxima method with a fitting to the Generalized Extreme Value (GEV) distribution before being transformed into the Frechet Z margin units. The calculation of the extreme coefficient resulted in a value between 1.4 to 1.85, indicating a relationship between the locations. Next, the best trend surface model was determined, which involves latitude coordinates for the calculation of the location parameter and both longitude and latitude coordinates for the calculation of the scale parameter. The spatial parameter estimation is carried using the powered exponential correlation function. Then, model validation was carried out using MAPE based on a comparison of return levels and testing data. The MAPE values obtained was 22.61% for the BFGS iteration method. The final step is to calculate return levels for periods of 2, 4, 6, 8, and 10 years ahead. All the results were categorized under very heavy rain. These results can be used by related parties to carry out disaster mitigation efforts.

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